library(magrittr)
library(tidyverse)
library(Seurat)
library(readxl)
library(cowplot)
library(colorblindr)
library(viridis)
library(progeny)
theme_cowplot2 <- function(...) {
theme_cowplot(font_size = 12, ...) %+replace%
theme(strip.background = element_blank(),
plot.background = element_blank())
}
theme_set(theme_cowplot2())
coi <- params$cell_type
cell_sort <- params$cell_sort
cell_type_major <- params$cell_type_major
louvain_resolution <- params$louvain_resolution
### load all data ---------------------------------
helper_f <- function(x) ifelse(is.na(x), "", x)
markers_v6 <- yaml::read_yaml("/home/uhlitzf/spectrum_tme/_data/small/signatures/hgsc_v6_major.yaml")
helper_f2 <- function(x) select(unnest(enframe(x, "subtype", "gene"), cols = gene), gene, subtype)
markers_v6_super <- lapply(yaml::read_yaml("/home/uhlitzf/spectrum_tme/_data/small/signatures/hgsc_v6_super.yaml"), helper_f2)
clrs <- yaml::read_yaml("/home/uhlitzf/spectrum_tme/_data/small/signatures/hgsc_v6_colors.yaml") %>%
lapply(function(x) map_depth(x, vec_depth(x)-2, unlist))
names(clrs$patient_id) <- str_remove_all(names(clrs$patient_id), "SPECTRUM-OV-")
meta_tbl <- read_excel("_data/small/MSK SPECTRUM - Single cell RNA-seq_v6.xlsx", sheet = 2) %>%
mutate(patient_id = str_remove_all(patient_id, "SPECTRUM-OV-")) %>%
filter(therapy == "pre-Rx")
signature_tbl <- read_tsv("_data/small/mutational_signatures_summary.tsv") %>%
mutate(patient_id = str_remove_all(patient_id, "SPECTRUM-OV-")) %>%
select(patient_id, consensus_signature) %>%
bind_rows(tibble(patient_id = unique(sort(meta_tbl$patient_id[!(meta_tbl$patient_id %in% .$patient_id)])), consensus_signature = "NA")) %>%
mutate(consensus_signature = ordered(consensus_signature, levels = names(clrs$consensus_signature))) %>%
arrange(consensus_signature)
seu_obj <- read_rds(paste0("/work/shah/isabl_data_lake/analyses/16/52/1652/celltypes/", coi, "_processed.rds"))
myfeatures <- c("UMAP_1", "UMAP_2", "umapharmony_1", "umapharmony_2", "sample", "RNA_snn_res.0.1", "RNA_snn_res.0.2", "RNA_snn_res.0.3", "doublet", "nCount_RNA", "nFeature_RNA", "percent.mt", "doublet_score")
plot_data <- as_tibble(FetchData(seu_obj, myfeatures)) %>%
left_join(select(meta_tbl, sample = isabl_id, patient_id, tumor_supersite, tumor_subsite, sort_parameters, therapy),
by = "sample") %>%
mutate(patient_id = str_remove_all(patient_id, "SPECTRUM-OV-"),
RNA_snn_res.0.1 = as.character(RNA_snn_res.0.1),
RNA_snn_res.0.2 = as.character(RNA_snn_res.0.2),
RNA_snn_res.0.3 = as.character(RNA_snn_res.0.3),
tumor_supersite = ordered(tumor_supersite, levels = names(clrs$tumor_supersite))) %>%
mutate(cell_id = colnames(seu_obj)) %>%
left_join(signature_tbl, by = "patient_id")
patient_id <- sort(unique(plot_data$patient_id))
COL1A1, COL3A1, WT1, ACTA2, CAV1, COL1A2, DCN, SPARC, COL6A1, CCDC80, LUM, COL6A2, COL6A3, CALD1, RARRES2, MGP, CTHRC1, AEBP1, POSTN, COL5A2, FBLN1, TAGLN, C1S, C1R, NNMT, MMP2, IGFBP5, TIMP1, FN1, IGFBP7, C3, COL5A1, LGALS1
markers_v6_super[[coi]] %>%
group_by(subtype) %>%
mutate(rank = row_number(gene)) %>%
spread(subtype, gene) %>%
mutate_all(.funs = helper_f) %>%
formattable::formattable()
| rank | Activated.IGF1.CAF | Activated.TGFb.CAF | Angiogenic.VEGF.CAF | Cycling.CAF | Early.CAF | Mesothelial.IL1.CAF | Pericyte |
|---|---|---|---|---|---|---|---|
| 1 | APOE | ACTA2 | ANGPTL4 | CDC20 | ACKR4 | AQP1 | A2M |
| 2 | ATF3 | COL11A1 | BNIP3 | CDK1 | APOD | C3 | ACTA2 |
| 3 | CYR61 | COL12A1 | CA12 | MKI67 | C7 | CALB2 | ADIRF |
| 4 | FBLN1 | COL5A1 | CA7 | PTTG1 | CFD | CCDC80 | CAV1 |
| 5 | IGF1 | COL5A2 | EGLN3 | TOP2A | GSN | CLDN1 | CCDC102B |
| 6 | COL6A1 | ENO1 | MGP | CXCL1 | COL4A1 | ||
| 7 | COL6A3 | ENO2 | PEG3 | EZR | COL4A2 | ||
| 8 | FAP | HILPDA | HAS1 | CRIP1 | |||
| 9 | FN1 | LDHA | HP | HIGD1B | |||
| 10 | MMP11 | VEGFA | KRT18 | IGFBP7 | |||
| 11 | MMP13 | KRT19 | MCAM | ||||
| 12 | KRT8 | MEF2C | |||||
| 13 | PRG4 | MHY11 | |||||
| 14 | SLPI | NDUFA4L2 | |||||
| 15 | UPK3B | PP1R14A | |||||
| 16 | RGS5 |
marker_tbl <- read_tsv(paste0("/work/shah/isabl_data_lake/analyses/16/52/1652/celltypes/", coi, "_markers.tsv")) %>%
filter(resolution == louvain_resolution)
## Hypergeometric test --------------------------------------
test_set <- marker_tbl %>%
group_by(cluster) %>%
filter(!str_detect(gene, "^RPS|^RPL")) %>%
slice(1:50) %>%
mutate(k = length(cluster)) %>%
ungroup %>%
select(cluster, gene, k) %>%
mutate(join_helper = 1) %>%
group_by(cluster, join_helper, k) %>%
nest(test_set = gene)
markers_doub_tbl <- markers_v6 %>%
enframe("subtype", "gene") %>%
filter(!(subtype %in% unique(c(coi, cell_type_major)))) %>%
unnest(gene) %>%
group_by(gene) %>%
filter(length(gene) == 1) %>%
mutate(subtype = paste0("doublet.", subtype)) %>%
bind_rows(tibble(subtype = "Mito.high", gene = grep("^MT-", rownames(seu_obj), value = T)))
ref_set <- markers_v6_super[[coi]] %>%
bind_rows(markers_doub_tbl) %>%
group_by(subtype) %>%
mutate(m = length(gene),
n = length(rownames(seu_obj))-m,
join_helper = 1) %>%
group_by(subtype, m, n, join_helper) %>%
nest(ref_set = gene)
hyper_tbl <- test_set %>%
left_join(ref_set, by = "join_helper") %>%
group_by(cluster, subtype, m, n, k) %>%
do(q = length(intersect(unlist(.$ref_set), unlist(.$test_set)))) %>%
mutate(pval = 1-phyper(q = q, m = m, n = n, k = k)) %>%
ungroup %>%
mutate(qval = p.adjust(pval, "BH"),
sig = qval < 0.01)
# hyper_tbl %>%
# group_by(subtype) %>%
# filter(any(qval < 0.01)) %>%
# ggplot(aes(subtype, -log10(qval), fill = sig)) +
# geom_bar(stat = "identity") +
# facet_wrap(~cluster) +
# coord_flip()
low_rank <- str_detect(unique(hyper_tbl$subtype), "doublet")
subtype_lvl <- c(sort(unique(hyper_tbl$subtype)[!low_rank]), sort(unique(hyper_tbl$subtype)[low_rank]))
cluster_label_tbl <- hyper_tbl %>%
mutate(subtype = ordered(subtype, levels = subtype_lvl)) %>%
arrange(qval, subtype) %>%
group_by(cluster) %>%
slice(1) %>%
mutate(subtype = ifelse(sig, as.character(subtype), paste0("unknown_", cluster))) %>%
select(cluster, cluster_label = subtype) %>%
ungroup %>%
mutate(cluster_label = make.unique(cluster_label, sep = "_"))
seu_obj$cluster_label <- unname(deframe(cluster_label_tbl)[as.character(unlist(seu_obj[[paste0("RNA_snn_res.", louvain_resolution)]]))])
plot_data$cluster_label <- seu_obj$cluster_label
marker_sheet <- marker_tbl %>%
left_join(cluster_label_tbl, by = "cluster") %>%
group_by(cluster_label) %>%
filter(!str_detect(gene, "^RPS|^RPL")) %>%
slice(1:50) %>%
mutate(rank = row_number(-avg_logFC)) %>%
select(cluster_label, gene, rank) %>%
spread(cluster_label, gene) %>%
mutate_all(.funs = helper_f)
formattable::formattable(marker_sheet)
| rank | Activated.IGF1.CAF | Activated.TGFb.CAF | Angiogenic.VEGF.CAF | Cycling.CAF | doublet.Endothelial.cell | doublet.Monocyte | doublet.Ovarian.cancer.cell | Early.CAF | Mesothelial.IL1.CAF | Pericyte |
|---|---|---|---|---|---|---|---|---|---|---|
| 1 | APOE | MMP11 | ANGPTL4 | CENPF | FABP4 | SPP1 | MMP7 | CFD | HP | COL4A1 |
| 2 | IGF1 | CTHRC1 | HILPDA | TOP2A | VWF | CCL4 | TFPI2 | C7 | SLPI | RGS5 |
| 3 | CYR61 | POSTN | NDRG1 | H2AFZ | PECAM1 | SRGN | SPINT2 | APOD | PRG4 | COL4A2 |
| 4 | ATF3 | CTSK | VEGFA | MKI67 | PLVAP | C1QB | KRT7 | GSN | PLA2G2A | NDUFA4L2 |
| 5 | FBLN1 | COL11A1 | PLIN2 | TUBA1B | EGFL7 | C1QA | CLDN3 | MGP | KRT19 | MCAM |
| 6 | IER2 | COL6A1 | LOX | NUSAP1 | RBP7 | TYROBP | EPCAM | PEG3 | EZR | PPP1R14A |
| 7 | C3 | FN1 | SERPINE1 | HMGN2 | CLDN5 | CXCR4 | GPRC5A | WISP2 | UPK3B | IGFBP7 |
| 8 | ZFP36 | COL12A1 | BNIP3 | PTTG1 | CD93 | CCL5 | LCN2 | ADH1B | KRT18 | CCDC102B |
| 9 | FOS | COL1A2 | IGFBP3 | STMN1 | ADGRL4 | RGS1 | CD24 | SFRP1 | CALB2 | A2M |
| 10 | CCDC80 | COL6A3 | ENO1 | BIRC5 | CLEC14A | CCL3 | KRT19 | IGFBP5 | TM4SF1 | CRIP1 |
| 11 | MGP | RGCC | GAPDH | ASPM | PCDH17 | C1QC | C19orf33 | RBP1 | KRT8 | MEF2C |
| 12 | JUN | VCAN | LDHA | PCLAF | CD34 | PTPRC | IGKC | ABCA8 | C3 | ACTA2 |
| 13 | FOSB | MMP13 | ERO1A | CCNB1 | ITGA6 | FCER1G | CLDN4 | GPX3 | CCDC80 | COL18A1 |
| 14 | WNT4 | MAFB | HSPA5 | TPX2 | CDH5 | LAPTM5 | WFDC2 | TNXB | ITLN1 | NOTCH3 |
| 15 | IER3 | TDO2 | MT2A | TYMS | SRGN | LYZ | TACSTD2 | AKAP12 | PROCR | ADIRF |
| 16 | RARRES1 | COL1A1 | FTH1 | CDKN3 | MMRN1 | CCL4L2 | CD9 | BTG2 | CLDN1 | HIGD1B |
| 17 | EGR1 | COL5A2 | PLOD2 | HMGB1 | PTPRB | AIF1 | MAL2 | ITM2A | TIMP1 | C11orf96 |
| 18 | CXCL2 | AEBP1 | CA9 | HMGB2 | TM4SF18 | NKG7 | ELF3 | PRELP | AQP1 | ITGA1 |
| 19 | APOC1 | ISLR | TGFBI | SMC4 | FLT1 | AREG | SMIM22 | JUNB | PLAT | MYH11 |
| 20 | DUSP1 | PLAU | PGK1 | CKS2 | EMCN | FCGR3A | MUC1 | FHL2 | SLC39A8 | ANGPT2 |
| 21 | SERPINE1 | GJA1 | EGLN3 | TUBB | RAMP3 | CD2 | CST6 | NR2F2 | WFDC2 | PDGFA |
| 22 | EFEMP1 | CXCL14 | ADM | DLGAP5 | SOX18 | ITGB2 | H2AFJ | SCN7A | SAA1 | LHFPL6 |
| 23 | CLU | PMEPA1 | DDIT4 | UBE2C | LMO2 | HCST | PON2 | LTBP4 | PTGIS | GJC1 |
| 24 | CXCL12 | COLEC12 | CLEC2B | TUBB4B | ESAM | LTB | MSLN | FXYD1 | S100A10 | EPS8 |
| 25 | CTGF | THY1 | NAMPT | CKAP2 | GIMAP7 | CD14 | CP | ABCA6 | SAT1 | PDGFRB |
| 26 | JUNB | INHBA | SLC2A1 | CENPE | APLNR | CD52 | TCIM | COL14A1 | HLA-DRB1 | ADGRF5 |
| 27 | KLF6 | DERL3 | TMEM158 | TUBA1C | ICAM2 | HLA-DQB1 | SFTA2 | ZBTB20 | CXCL6 | FAM162B |
| 28 | RSPO3 | SEPT11 | FAM162A | UBE2S | MMRN2 | CD69 | MAL | LEPR | KRT7 | UACA |
| 29 | ALDH2 | COL5A1 | CA12 | JPT1 | NOTCH4 | GZMA | UCA1 | DCN | SOD2 | PLAC9 |
| 30 | COL14A1 | NTM | LOXL2 | KPNA2 | ROBO4 | CORO1A | TPI1 | NR4A1 | RARRES1 | NR2F2 |
| 31 | NFKBIZ | LUM | SLC16A3 | NUCKS1 | CXorf36 | HLA-DQA1 | AGR2 | FOSB | KLK11 | CAV1 |
| 32 | LXN | SULF1 | HSP90B1 | SMC2 | SLCO2A1 | GNLY | PERP | FOS | MAF | CARMN |
| 33 | PTGIS | DIO2 | P4HA1 | TK1 | ECSCR | FYB1 | SPINK1 | ALDH1A1 | DAB2 | TBX2 |
| 34 | KIAA1324L | COL10A1 | CPE | KIF20B | NOSTRIN | SAMSN1 | UPK1B | NR2F1 | CA12 | TINAGL1 |
| 35 | SELENOP | COL3A1 | P4HB | HMMR | GIMAP4 | CD3D | CLDN7 | SERPINE2 | RARRES3 | STEAP4 |
| 36 | SEMA3C | ISG15 | BNIP3L | ARL6IP1 | CCL14 | SLA | S100A9 | ABCA10 | CFB | HES4 |
| 37 | PDLIM3 | FAP | MT3 | PBK | CYYR1 | LCP1 | HMGA1 | SFRP4 | SELENOP | MYL9 |
| 38 | SPARCL1 | ANTXR1 | P4HA2 | ANLN | PCAT19 | ALOX5AP | MUC16 | KIAA1551 | CEMIP | COX4I2 |
| 39 | HSPA1A | PRRX1 | ZFAS1 | H2AFV | FAM167B | TRBC2 | ERP27 | EGR1 | CYB5A | GUCY1A2 |
| 40 | C7 | TCF4 | ERRFI1 | CCNB2 | TIE1 | CD53 | BCAM | SPARCL1 | DPYS | PTP4A3 |
| 41 | NFKBIA | TMEM158 | CHI3L2 | RAN | MCTP1 | MS4A6A | CRIP1 | ABLIM1 | CXCL1 | JAG1 |
| 42 | TXNIP | MDK | THBS2 | MAD2L1 | MYCT1 | CD48 | VAMP8 | DEPP1 | IL6ST | TAGLN |
| 43 | ADAMTS1 | HTRA1 | TMEM45A | CDK1 | PREX2 | GPR183 | ITGB8 | JUN | FLRT2 | SEPT4 |
| 44 | METTL7A | SPARC | TPI1 | GTSE1 | GIMAP1 | GZMB | FXYD3 | TCEAL4 | VTN | SOD3 |
| 45 | CADM3 | SDC1 | AL133453.1 | SGO2 | MECOM | CYTIP | S100A14 | PDK4 | ITGB8 | TFPI |
| 46 | GALNT15 | MARCKS | PTX3 | RRM2 | STAB1 | CD37 | SCNN1A | TXNIP | SGK1 | S100A4 |
| 47 | TSHZ2 | OLFML2B | LMAN1 | CEP55 | S1PR1 | CD3G | S100A1 | TSHZ2 | CYSTM1 | WFDC1 |
| 48 | NR4A1 | PCOLCE | SLC39A14 | HELLS | KANK3 | GMFG | UGT2B7 | LAMA2 | CLU | PGF |
| 49 | PIEZO2 | PALLD | PDIA6 | DEK | ADCY4 | CD163 | TMEM238 | STAR | GALNT1 | SPARCL1 |
| 50 | NFIB | APCDD1 | CD44 | DTYMK | ESM1 | TRBC1 | CAPS | ZFP36 | ST3GAL5 | ENPEP |
write_tsv(marker_sheet, paste0("/work/shah/uhlitzf/data/SPECTRUM/freeze/v6/", coi, "_marker_sheet.tsv"))
enframe(sort(table(seu_obj$cluster_label))) %>%
mutate(name = ordered(name, levels = rev(name))) %>%
ggplot() +
geom_bar(aes(name, value), stat = "identity") +
theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5)) +
labs(y = c("#cells"), x = "cluster")
alpha_lvl <- ifelse(nrow(plot_data) < 20000, 0.2, 0.1)
pt_size <- ifelse(nrow(plot_data) < 20000, 0.2, 0.05)
common_layers_disc <- list(
geom_point(size = pt_size, alpha = alpha_lvl),
NoAxes(),
guides(color = guide_legend(override.aes = list(size = 2, alpha = 1))),
labs(color = "")
)
common_layers_cont <- list(
geom_point(size = pt_size, alpha = alpha_lvl),
NoAxes(),
scale_color_gradientn(colors = viridis(9)),
guides(color = guide_colorbar())
)
ggplot(plot_data, aes(umapharmony_1, umapharmony_2, color = cluster_label)) +
common_layers_disc +
#facet_wrap(~therapy) +
ggtitle("Sub cluster")
my_subtypes <- names(clrs$cluster_label[[coi]])
my_subtypes <- c(my_subtypes, unlist(lapply(paste0("_", 1:3), function(x) paste0(my_subtypes, x)))) %>% .[!str_detect(., "doublet")]
cells_to_keep <- colnames(seu_obj)[seu_obj$cluster_label %in% my_subtypes]
# seu_obj_sub <- subset(seu_obj, cells = cells_to_keep)
# seu_obj_sub <- RunUMAP(seu_obj_sub, dims = 1:50, reduction = "harmony", reduction.name = "umapharmony", reduction.key = "umapharmony_")
# seu_obj_sub$cluster_label <- seu_obj$cluster_label[colnames(seu_obj) %in% colnames(seu_obj_sub)]
# write_rds(seu_obj_sub, paste0("/work/shah/uhlitzf/data/SPECTRUM/freeze/v6/", coi, "_processed_filtered.rds"))
seu_obj_sub <- read_rds(paste0("/work/shah/uhlitzf/data/SPECTRUM/freeze/v6/", coi, "_processed_filtered.rds"))
plot_data_sub <- as_tibble(FetchData(seu_obj_sub, c(myfeatures, "cluster_label"))) %>%
left_join(select(meta_tbl, sample = isabl_id, patient_id, tumor_supersite, tumor_subsite, sort_parameters, therapy),
by = "sample") %>%
mutate(patient_id = str_remove_all(patient_id, "SPECTRUM-OV-"),
tumor_supersite = ordered(tumor_supersite, levels = names(clrs$tumor_supersite))) %>%
mutate(cell_id = colnames(seu_obj_sub),
cluster_label = ordered(cluster_label, levels = my_subtypes),
) %>%
left_join(signature_tbl, by = "patient_id")
if (cell_sort == "CD45+") {
plot_data_sub <- filter(plot_data_sub, sort_parameters != "singlet, live, CD45-", !is.na(tumor_supersite))
}
if (cell_sort == "CD45-") {
plot_data_sub <- filter(plot_data_sub, sort_parameters != "singlet, live, CD45+", !is.na(tumor_supersite))
}
ggplot(plot_data_sub, aes(umapharmony_1, umapharmony_2, color = cluster_label)) +
common_layers_disc +
ggtitle("Sub cluster") +
#facet_wrap(~cluster_label) +
scale_color_manual(values = clrs$cluster_label[[coi]])
ggplot(plot_data_sub, aes(umapharmony_1, umapharmony_2, color = patient_id)) +
common_layers_disc +
ggtitle("Patient") +
# facet_wrap(~therapy) +
scale_color_manual(values = clrs$patient_id)
ggplot(plot_data_sub, aes(umapharmony_1, umapharmony_2, color = tumor_supersite)) +
# geom_point(aes(umapharmony_1, umapharmony_2),
# color = "grey90", size = 0.01,
# data = select(plot_data_sub, -tumor_supersite)) +
common_layers_disc +
ggtitle("Site") +
# facet_wrap(~therapy) +
scale_color_manual(values = clrs$tumor_supersite)
write_tsv(select(plot_data_sub, cell_id, everything(), -UMAP_1, -UMAP_2, -contains("RNA_")), paste0("/work/shah/uhlitzf/data/SPECTRUM/freeze/v6/", coi, "_embedding.tsv"))
signature_modules <- read_excel("_data/small/signatures/SPECTRUM v5 sub cluster markers.xlsx", sheet = 2, skip = 1, range = "M2:P100") %>%
gather(module, gene) %>%
na.omit() %>%
group_by(module) %>%
do(gene = c(.$gene)) %>%
{setNames(.$gene, .$module)}
signature_modules$ISG.module <- c("CCL5", "CXCL10", "IFNA1", "IFNB1", "ISG15", "IFI27L2", "SAMD9L")
## compute expression module scores
for (i in 1:length(signature_modules)) {
seu_obj_sub <- AddModuleScore(seu_obj_sub, features = signature_modules[i], name = names(signature_modules)[i])
seu_obj_sub[[names(signature_modules)[i]]] <- seu_obj_sub[[paste0(names(signature_modules)[i], "1")]]
seu_obj_sub[[paste0(names(signature_modules)[i], "1")]] <- NULL
print(paste(names(signature_modules)[i], "DONE"))
}
## [1] "CD8.Cytotoxic DONE"
## [1] "CD8.Dysfunctional DONE"
## [1] "CD8.Naive DONE"
## [1] "CD8.Predysfunctional DONE"
## [1] "ISG.module DONE"
## compute progeny scores
progeny_list <- seu_obj_sub@assays$RNA@data[VariableFeatures(seu_obj_sub),] %>%
as.matrix %>%
progeny %>%
as.data.frame %>%
as.list
names(progeny_list) <- make.names(paste0(names(progeny_list), ".pathway"))
for (i in 1:length(progeny_list)) {
seu_obj_sub <- AddMetaData(seu_obj_sub, metadata = progeny_list[[i]],
col.name = names(progeny_list)[i])
}
write_rds(seu_obj_sub, paste0("/work/shah/uhlitzf/data/SPECTRUM/freeze/v6/", coi, "_processed_filtered.rds"))
marker_top_tbl <- marker_sheet[,-1] %>%
slice(1:10) %>%
as.list %>%
.[!str_detect(names(.), "doublet")] %>%
enframe("cluster_label_x", "gene") %>%
unnest(gene)
plot_data_markers <- as_tibble(FetchData(seu_obj_sub, c("cluster_label", myfeatures, unique(marker_top_tbl$gene)))) %>%
gather(gene, value, -c(1:(length(myfeatures)+1))) %>%
left_join(select(meta_tbl, sample = isabl_id, patient_id, tumor_supersite, tumor_subsite, sort_parameters, therapy),
by = "sample") %>%
mutate(patient_id = str_remove_all(patient_id, "SPECTRUM-OV-"),
tumor_supersite = ordered(tumor_supersite, levels = names(clrs$tumor_supersite))) %>%
mutate(cluster_label = ordered(cluster_label, levels = my_subtypes)) %>%
group_by(cluster_label, gene) %>%
summarise(value = mean(value, na.rm = T)) %>%
group_by(gene) %>%
mutate(value = scales::rescale(value)) %>%
left_join(marker_top_tbl, by = "gene") %>%
mutate(cluster_label_x = ordered(cluster_label_x, levels = rev(names(clrs$cluster_label[[coi]]))))
ggplot(plot_data_markers) +
geom_tile(aes(gene, cluster_label, fill = value)) +
theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5)) +
facet_grid(~cluster_label_x, scales = "free", space = "free") +
scale_fill_gradientn(colors = viridis(9)) +
labs(fill = "Scaled\nexpression") +
theme(aspect.ratio = 1,
axis.line = element_blank(),
axis.ticks = element_blank(),
axis.title = element_blank())
# ggsave(paste0("_fig/002_marker_heatmap_", coi, ".pdf"), width = nrow(marker_top_tbl)/6, height = 5)
comp_site_tbl <- plot_data_sub %>%
filter(!is.na(tumor_supersite)) %>%
group_by(cluster_label, tumor_supersite) %>%
tally %>%
group_by(tumor_supersite) %>%
mutate(nrel = n/sum(n)*100) %>%
ungroup
pnrel_site <- ggplot(comp_site_tbl) +
geom_bar(aes(tumor_supersite, nrel, fill = cluster_label),
stat = "identity", position = position_stack()) +
scale_y_continuous(expand = c(0, 0)) +
theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5)) +
labs(fill = "Cluster", y = "Fraction [%]", x = "") +
scale_fill_manual(values = clrs$cluster_label[[coi]])
pnabs_site <- ggplot(comp_site_tbl) +
geom_bar(aes(tumor_supersite, n, fill = cluster_label),
stat = "identity", position = position_stack()) +
scale_y_continuous(expand = c(0, 0)) +
theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5)) +
labs(fill = "Cluster", y = "# cells", x = "") +
scale_fill_manual(values = clrs$cluster_label[[coi]])
plot_grid(pnabs_site, pnrel_site, ncol = 2, align = "h")
# ggsave(paste0("_fig/02_deep_dive_", coi, "_comp_site.pdf"), width = 8, height = 4)
comp_tbl_sample_sort <- plot_data_sub %>%
group_by(tumor_subsite, tumor_supersite, patient_id, consensus_signature, therapy, cluster_label) %>%
tally %>%
group_by(tumor_subsite, tumor_supersite, patient_id, consensus_signature, therapy) %>%
mutate(nrel = n/sum(n)*100,
nsum = sum(n),
log10n = log10(n),
label_supersite = "Site",
label_mutsig = "Signature",
label_therapy = "Rx") %>%
ungroup %>%
arrange(desc(therapy), tumor_supersite) %>%
mutate(tumor_subsite_rx = paste0(tumor_subsite, "_", therapy)) %>%
mutate(tumor_subsite = ordered(tumor_subsite, levels = unique(tumor_subsite)),
tumor_subsite_rx = ordered(tumor_subsite_rx, levels = unique(tumor_subsite_rx))) %>%
arrange(patient_id) %>%
mutate(label_patient_id = ifelse(as.logical(as.numeric(fct_inorder(as.character(patient_id)))%%2), "Patient1", "Patient2"))
sample_id_x_tbl <- plot_data_sub %>%
mutate(sort_short_x = cell_sort) %>%
distinct(patient_id, sort_short_x, tumor_subsite, therapy, sample) %>%
unite(sample_id_x, patient_id, sort_short_x, tumor_subsite, therapy) %>%
arrange(sample_id_x)
comp_tbl_sample_sort %>%
mutate(sort_short_x = cell_sort) %>%
unite(sample_id_x, patient_id, sort_short_x, tumor_subsite_rx) %>%
select(sample_id_x, cluster_label, n, nrel, nsum) %>%
left_join(sample_id_x_tbl, by = "sample_id_x") %>%
write_tsv(paste0("/work/shah/uhlitzf/data/SPECTRUM/freeze/v6/", coi, "_subtype_compositions.tsv"))
ybreaks <- c(500, 1000, 2000, 4000, 6000, 8000, 10000, 15000, 20000)
max_cells_per_sample <- max(comp_tbl_sample_sort$nsum)
ymaxn <- ybreaks[max_cells_per_sample < ybreaks][1]
comp_plot_wrapper <- function(y = "nrel", switch = NULL) {
if (y == "nrel") ylab <- paste0("Fraction\nof cells [%]")
if (y == "n") ylab <- paste0("Number\nof cells")
p <- ggplot(comp_tbl_sample_sort,
aes_string("tumor_subsite_rx", y, fill = "cluster_label")) +
facet_grid(~patient_id, space = "free", scales = "free", switch = switch) +
coord_cartesian(clip = "off") +
scale_fill_manual(values = clrs$cluster_label[[coi]]) +
theme(axis.text.x = element_blank(),
axis.title.y = element_text(angle = 0, vjust = 0.5, hjust = 0.5,
margin = margin(0, -0.4, 0, 0, unit = "npc")),
axis.ticks.x = element_blank(),
axis.title.x = element_blank(),
axis.line.x = element_blank(),
strip.text.y = element_blank(),
strip.text.x = element_blank(),
strip.background.y = element_blank(),
strip.background.x = element_blank(),
plot.margin = margin(0, 0, 0, 0, "npc")) +
labs(x = "",
y = ylab) +
guides(fill = FALSE)
if (y == "nrel") p <- p +
geom_bar(stat = "identity") +
scale_y_continuous(expand = c(0, 0),
breaks = c(0, 50, 100),
labels = c("0", "50", "100"))
if (y == "n") p <- p +
geom_bar(stat = "identity", position = position_stack()) +
scale_y_continuous(expand = c(0, 0),
breaks = c(0, ymaxn/2, ymaxn),
limits = c(0, ymaxn),
labels = c("", ymaxn/2, ymaxn)) +
expand_limits(y = c(0, ymaxn)) +
theme(panel.grid.major.y = element_line(linetype = 1, color = "grey90", size = 0.5))
return(p)
}
common_label_layers <- list(
geom_tile(color = "white", size = 0.15),
theme(axis.text.x = element_blank(),
axis.ticks = element_blank(),
axis.title.x = element_blank(),
axis.line.x = element_blank(),
strip.text = element_blank(),
strip.background = element_blank(),
plot.margin = margin(0, 0, 0, 0, "npc")),
scale_y_discrete(expand = c(0, 0)),
labs(y = ""),
guides(fill = FALSE),
facet_grid(~patient_id,
space = "free", scales = "free")
)
comp_label_site <- ggplot(distinct(comp_tbl_sample_sort, tumor_subsite_rx, therapy, tumor_supersite, label_supersite, patient_id),
aes(tumor_subsite_rx, label_supersite,
fill = tumor_supersite)) +
scale_fill_manual(values = clrs$tumor_supersite) +
common_label_layers
comp_label_rx <- ggplot(distinct(comp_tbl_sample_sort, tumor_subsite_rx, therapy, tumor_supersite, label_therapy, consensus_signature, patient_id),
aes(tumor_subsite_rx, label_therapy,
fill = therapy)) +
scale_fill_manual(values = c(`post-Rx` = "gold3", `pre-Rx` = "steelblue")) +
common_label_layers
comp_label_mutsig <- ggplot(distinct(comp_tbl_sample_sort, tumor_subsite_rx, therapy, tumor_supersite, label_mutsig, consensus_signature, patient_id),
aes(tumor_subsite_rx, label_mutsig,
fill = consensus_signature)) +
scale_fill_manual(values = clrs$consensus_signature) +
common_label_layers
patient_label_tbl <- distinct(comp_tbl_sample_sort, patient_id, .keep_all = T)
comp_label_patient_id <- ggplot(patient_label_tbl, aes(tumor_subsite_rx, label_patient_id)) +
scale_fill_manual(values = clrs$consensus_signature) +
geom_text(aes(tumor_subsite_rx, label_patient_id, label = patient_id)) +
facet_grid(~patient_id,
space = "free", scales = "free") +
coord_cartesian(clip = "off") +
theme_void() +
theme(strip.text = element_blank(),
strip.background = element_blank(),
plot.margin = margin(0, 0, 0, 0, "npc"))
hist_plot_wrapper <- function(x = "nrel") {
if (x == "nrel") {
xlab <- paste0("Fraction of cells [%]")
bw <- 5
}
if (x == "log10n") {
xlab <- paste0("Number of cells")
bw <- 0.2
}
p <- ggplot(comp_tbl_sample_sort) +
ggridges::geom_density_ridges(
aes_string(x, "cluster_label", fill = "cluster_label"), color = "black",
stat = "binline", binwidth = bw, scale = 3) +
facet_grid(label_supersite~.,
space = "free", scales = "free") +
scale_fill_manual(values = clrs$cluster_label[[coi]]) +
theme(axis.text.y = element_blank(),
axis.ticks.y = element_blank(),
axis.title.y = element_blank(),
axis.line.y = element_blank(),
strip.text = element_blank(),
strip.background = element_blank(),
plot.margin = margin(0, 0, 0, 0, "npc")) +
scale_x_continuous(expand = c(0, 0)) +
scale_y_discrete(expand = c(0, 0)) +
guides(fill = FALSE) +
labs(x = xlab)
if (x == "log10n") p <- p + expand_limits(x = c(0, 3)) +
scale_x_continuous(expand = c(0, 0),
labels = function(x) parse(text = paste("10^", x)))
return(p)
}
pcomp1 <- comp_plot_wrapper("n")
pcomp2 <- comp_plot_wrapper("nrel")
pcomp_grid <- plot_grid(comp_label_patient_id,
pcomp1, pcomp2,
comp_label_site, comp_label_rx, comp_label_mutsig,
ncol = 1, align = "v",
rel_heights = c(0.15, 0.33, 0.33, 0.06, 0.06, 0.06))
phist1 <- hist_plot_wrapper("log10n")
pcomp_hist_grid <- ggdraw() +
draw_plot(pcomp_grid, x = 0.01, y = 0, width = 0.85, height = 1) +
draw_plot(phist1, x = 0.87, y = 0.05, width = 0.12, height = 0.8)
pcomp_hist_grid
# ggsave(paste0("_fig/02_composition_v6_",coi,".pdf"), pcomp_hist_grid, width = 10, height = 2)
comp_tbl_z <- comp_tbl_sample_sort %>%
filter(therapy == "pre-Rx",
!(tumor_supersite %in% c("Ascites", "Other"))) %>%
group_by(patient_id, cluster_label) %>%
arrange(patient_id, cluster_label, nrel) %>%
mutate(rank = row_number(nrel),
z_rank = scales::rescale(rank)) %>%
mutate(mean_nrel = mean(nrel, na.rm = T),
sd_nrel = sd(nrel, na.rm = T),
z_nrel = (nrel - mean_nrel) / sd_nrel) %>%
ungroup()
ggplot(comp_tbl_z) +
geom_boxplot(aes(tumor_supersite, z_nrel, color = tumor_supersite),
outlier.shape = NA) +
geom_point(aes(tumor_supersite, z_nrel, color = tumor_supersite), position = position_jitter(), size = 0.1) +
facet_grid(~cluster_label, scales = "free_x", space = "free_x") +
theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5),
aspect.ratio = 5) +
scale_color_manual(values = clrs$tumor_supersite)
ggplot(comp_tbl_z) +
geom_boxplot(aes(tumor_supersite, z_rank, color = tumor_supersite),
outlier.shape = NA) +
geom_point(aes(tumor_supersite, z_rank, color = tumor_supersite), position = position_jitter(), size = 0.1) +
facet_grid(~cluster_label, scales = "free_x", space = "free_x") +
theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5),
aspect.ratio = 5) +
scale_color_manual(values = clrs$tumor_supersite)
devtools::session_info()
## ─ Session info ───────────────────────────────────────────────────────────────
## setting value
## version R version 3.6.2 (2019-12-12)
## os Debian GNU/Linux 10 (buster)
## system x86_64, linux-gnu
## ui X11
## language (EN)
## collate en_US.UTF-8
## ctype en_US.UTF-8
## tz Etc/UTC
## date 2020-12-05
##
## ─ Packages ───────────────────────────────────────────────────────────────────
## package * version date lib
## ape 5.3 2019-03-17 [2]
## assertthat 0.2.1 2019-03-21 [2]
## backports 1.1.10 2020-09-15 [1]
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## Biobase 2.46.0 2019-10-29 [2]
## BiocGenerics 0.32.0 2019-10-29 [2]
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## gbRd 0.4-11 2012-10-01 [2]
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## gplots 3.0.1.2 2020-01-11 [2]
## gridExtra 2.3 2017-09-09 [2]
## gtable 0.3.0 2019-03-25 [2]
## gtools 3.8.1 2018-06-26 [2]
## haven 2.3.1 2020-06-01 [1]
## hms 0.5.3 2020-01-08 [1]
## htmltools 0.4.0 2019-10-04 [2]
## htmlwidgets 1.5.1 2019-10-08 [2]
## httr 1.4.2 2020-07-20 [1]
## ica 1.0-2 2018-05-24 [2]
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## KernSmooth 2.23-16 2019-10-15 [3]
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## labeling 0.3 2014-08-23 [2]
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## CRAN (R 3.6.2)
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## [1] /home/uhlitzf/R/lib
## [2] /usr/local/lib/R/site-library
## [3] /usr/local/lib/R/library